Add async support to SelfQueryRetriever (langchain-ai#10175)

### Description

SelfQueryRetriever is missing async support, so I am adding it.
I also removed deprecated predict_and_parse method usage here, and added
some tests.

### Issue
N/A

### Tag maintainer
Not yet

### Twitter handle
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libs/langchain/langchain/chains/query_constructor/base.py

@@ -160,4 +160,6 @@ def load_query_constructor_chain(
         allowed_operators=allowed_operators,
         enable_limit=enable_limit,
     )
-    return LLMChain(llm=llm, prompt=prompt, **kwargs)
+    return LLMChain(
+        llm=llm, prompt=prompt, output_parser=prompt.output_parser, **kwargs
+    )

libs/langchain/langchain/retrievers/self_query/base.py

@@ -1,8 +1,11 @@
 """Retriever that generates and executes structured queries over its own data source."""
+import logging
+from typing import Any, Dict, List, Optional, Tuple, Type, cast
 
-from typing import Any, Dict, List, Optional, Type, cast
-
-from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
 from langchain.chains import LLMChain
 from langchain.chains.query_constructor.base import load_query_constructor_chain
 from langchain.chains.query_constructor.ir import StructuredQuery, Visitor
@@ -42,6 +45,8 @@
     Weaviate,
 )
 
+logger = logging.getLogger(__name__)
+
 
 def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
     """Get the translator class corresponding to the vector store class."""
@@ -108,6 +113,49 @@ def validate_translator(cls, values: Dict) -> Dict:
             )
         return values
 
+    def _get_structured_query(
+        self, inputs: Dict[str, Any], run_manager: CallbackManagerForRetrieverRun
+    ) -> StructuredQuery:
+        structured_query = cast(
+            StructuredQuery,
+            self.llm_chain.predict(callbacks=run_manager.get_child(), **inputs),
+        )
+        return structured_query
+
+    async def _aget_structured_query(
+        self, inputs: Dict[str, Any], run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> StructuredQuery:
+        structured_query = cast(
+            StructuredQuery,
+            await self.llm_chain.apredict(callbacks=run_manager.get_child(), **inputs),
+        )
+        return structured_query
+
+    def _prepare_query(
+        self, query: str, structured_query: StructuredQuery
+    ) -> Tuple[str, Dict[str, Any]]:
+        new_query, new_kwargs = self.structured_query_translator.visit_structured_query(
+            structured_query
+        )
+        if structured_query.limit is not None:
+            new_kwargs["k"] = structured_query.limit
+        if self.use_original_query:
+            new_query = query
+        search_kwargs = {**self.search_kwargs, **new_kwargs}
+        return new_query, search_kwargs
+
+    def _get_docs_with_query(
+        self, query: str, search_kwargs: Dict[str, Any]
+    ) -> List[Document]:
+        docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
+        return docs
+
+    async def _aget_docs_with_query(
+        self, query: str, search_kwargs: Dict[str, Any]
+    ) -> List[Document]:
+        docs = await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
+        return docs
+
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
@@ -120,25 +168,30 @@ def _get_relevant_documents(
             List of relevant documents
         """
         inputs = self.llm_chain.prep_inputs({"query": query})
-        structured_query = cast(
-            StructuredQuery,
-            self.llm_chain.predict_and_parse(
-                callbacks=run_manager.get_child(), **inputs
-            ),
-        )
+        structured_query = self._get_structured_query(inputs, run_manager)
         if self.verbose:
-            print(structured_query)
-        new_query, new_kwargs = self.structured_query_translator.visit_structured_query(
-            structured_query
-        )
-        if structured_query.limit is not None:
-            new_kwargs["k"] = structured_query.limit
+            logger.info(f"Generated Query: {structured_query}")
+        new_query, search_kwargs = self._prepare_query(query, structured_query)
+        docs = self._get_docs_with_query(new_query, search_kwargs)
+        return docs
 
-        if self.use_original_query:
-            new_query = query
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Get documents relevant for a query.
 
-        search_kwargs = {**self.search_kwargs, **new_kwargs}
-        docs = self.vectorstore.search(new_query, self.search_type, **search_kwargs)
+        Args:
+            query: string to find relevant documents for
+
+        Returns:
+            List of relevant documents
+        """
+        inputs = self.llm_chain.prep_inputs({"query": query})
+        structured_query = await self._aget_structured_query(inputs, run_manager)
+        if self.verbose:
+            logger.info(f"Generated Query: {structured_query}")
+        new_query, search_kwargs = self._prepare_query(query, structured_query)
+        docs = await self._aget_docs_with_query(new_query, search_kwargs)
         return docs
 
     @classmethod

libs/langchain/tests/unit_tests/retrievers/self_query/test_base.py

@@ -0,0 +1,142 @@
+from typing import Any, Dict, List, Tuple, Union
+
+import pytest
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+from langchain.chains.query_constructor.schema import AttributeInfo
+from langchain.retrievers import SelfQueryRetriever
+from langchain.schema import Document
+from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+
+class FakeTranslator(Visitor):
+    allowed_comparators = (
+        Comparator.EQ,
+        Comparator.NE,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.CONTAIN,
+        Comparator.LIKE,
+    )
+    allowed_operators = (Operator.AND, Operator.OR, Operator.NOT)
+
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        return f"${func.value}"
+
+    def visit_operation(self, operation: Operation) -> Dict:
+        args = [arg.accept(self) for arg in operation.arguments]
+        return {self._format_func(operation.operator): args}
+
+    def visit_comparison(self, comparison: Comparison) -> Dict:
+        return {
+            comparison.attribute: {
+                self._format_func(comparison.comparator): comparison.value
+            }
+        }
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"filter": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs
+
+
+class InMemoryVectorstoreWithSearch(InMemoryVectorStore):
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        res = self.store.get(query)
+        if res is None:
+            return []
+        return [res]
+
+
+@pytest.fixture()
+def fake_llm() -> FakeLLM:
+    return FakeLLM(
+        queries={
+            "1": """```json
+{
+    "query": "test",
+    "filter": null
+}
+```""",
+            "bar": "baz",
+        },
+        sequential_responses=True,
+    )
+
+
+@pytest.fixture()
+def fake_vectorstore() -> InMemoryVectorstoreWithSearch:
+    vectorstore = InMemoryVectorstoreWithSearch()
+    vectorstore.add_documents(
+        [
+            Document(
+                page_content="test",
+                metadata={
+                    "foo": "bar",
+                },
+            ),
+        ],
+        ids=["test"],
+    )
+    return vectorstore
+
+
+@pytest.fixture()
+def fake_self_query_retriever(
+    fake_llm: FakeLLM, fake_vectorstore: InMemoryVectorstoreWithSearch
+) -> SelfQueryRetriever:
+    return SelfQueryRetriever.from_llm(
+        llm=fake_llm,
+        vectorstore=fake_vectorstore,
+        document_contents="test",
+        metadata_field_info=[
+            AttributeInfo(
+                name="foo",
+                type="string",
+                description="test",
+            ),
+        ],
+        structured_query_translator=FakeTranslator(),
+    )
+
+
+def test__get_relevant_documents(fake_self_query_retriever: SelfQueryRetriever) -> None:
+    relevant_documents = fake_self_query_retriever._get_relevant_documents(
+        "foo",
+        run_manager=CallbackManagerForRetrieverRun.get_noop_manager(),
+    )
+    assert len(relevant_documents) == 1
+    assert relevant_documents[0].metadata["foo"] == "bar"
+
+
+@pytest.mark.asyncio
+async def test__aget_relevant_documents(
+    fake_self_query_retriever: SelfQueryRetriever,
+) -> None:
+    relevant_documents = await fake_self_query_retriever._aget_relevant_documents(
+        "foo",
+        run_manager=AsyncCallbackManagerForRetrieverRun.get_noop_manager(),
+    )
+    assert len(relevant_documents) == 1
+    assert relevant_documents[0].metadata["foo"] == "bar"